How to Be the Boss of Your AI Assistant
Understanding LLMs & The Research Workflow
Welcome!
Today’s Journey
- Understanding your AI assistant
- Why structure beats magic
- A proven 10-step research workflow
- Live demonstration
- Your roadmap to AI-powered research
Welcome students. Today we’re learning how to effectively manage AI tools for research. This isn’t about fancy prompts - it’s about understanding how these tools work.
Quick System Check! 📋
Do you have access to:
- ✅ Microsoft Copilot (copilot.microsoft.com)
- ✅ Internet connection
- ✅ Ability to download files
Can’t Access Copilot?
- Try ChatGPT (chat.openai.com) - free tier works
- Claude (claude.ai) - also free
- Key point: The principles work with ANY AI assistant
Timing: We’ll have troubleshooting breaks at 15 and 35 minutes!
The Problem: The “Everything” Prompt
Have you ever tried this?
- You ask AI to do a huge task all at once…
- “Analyse high-pressure processing for juice shelf life, tell me pros and cons, design an experiment for Vitamin C in orange juice, write the methods, and create expected results table”
What do you get back? 😕
- ❌ Shallow, generic summary
- ❌ Forgets half your instructions
- ❌ Messy, unusable output
Real Example: “Write my entire literature review on plant proteins” → Gets 2 paragraphs of Wikipedia-level content
This happens because you’re giving the AI too much to think about at once. It’s like asking someone to juggle while solving math problems.
The Solution: Think Like a Manager 🎯
Not a Magician! ✨
Big Idea: Break complex research into small, logical steps
The Golden Rule - One Task, One Prompt
Give your AI assistant one clear job at a time
Let’s explore why this simple rule is so powerful…
Quick Start Essentials 📸
The Most Important Prompt Template:
You are an AI research scientist specializing in [YOUR FIELD].
Task: [ONE SPECIFIC TASK]
Requirements:
- [SPECIFIC REQUIREMENT 1]
- [SPECIFIC REQUIREMENT 2]
- [SPECIFIC REQUIREMENT 3]
Format: [HOW YOU WANT THE OUTPUT]
Context: [BACKGROUND INFO IF NEEDED]
Save this! 80% of your AI interactions will use this basic structure
Reason 1: Limited “Working Memory” 🧠
❌ The “Everything” Prompt
Scribbling 10 problems at once = No room for solutions
✅ The Step-by-Step Approach
One problem, full space = Detailed, accurate results
Takeaway: A single, focused task gets the AI’s full attention
LLMs have a context window - think of it as their working memory. When you fill it with multiple complex tasks, each gets less attention and processing power.
Technical Detail: Context Windows 📊
What’s really happening?
- LLMs have finite context windows (8k-200k tokens)
- Token ≈ 0.75 words
- Each task competes for this limited space
Example:
- LLM Context window: ~8,000 tokens
- Complex research prompt: ~500 tokens
- Response space needed: ~1,500 tokens per task
- Result: Only 3-4 tasks fit properly!
Think of it like RAM: Too many programs = computer slows down. Too many tasks = AI quality drops.
This is why breaking tasks down isn’t just helpful - it’s technically necessary for quality outputs.
Reason 2: Guiding the AI’s “Thinking” 🧭
LLMs Create Answers Piece by Piece
- Each word depends on previous words
- Complex prompts = mental shortcuts
- Structured prompts = logical reasoning
Cooking Analogy 👨🍳
❌ Bad: “Make beef wellington”
Chef might skip steps or use wrong ingredients
✅ Good: 1. “First, sear the beef” 2. “Next, prepare duxelles” 3. “Then, wrap in pastry”
By giving step-by-step instructions, you force the AI to build a logical argument, leading to much stronger outputs.
Reason 3: Easy Error Detection & Fixing 🔧
With a Giant Prompt
- Problem: Weak experimental method
- Solution: Re-run EVERYTHING
- Time lost: 10-15 minutes
- Quality: Hope for the best 🤞
With Our Workflow
- Problem: Weak experimental method
- Solution: Fix just that step
- Time lost: 2 minutes
- Quality: Keep what works ✓
Benefit: Iterative refinement = Higher quality + Less frustration
Reason 4: More and Better Ideas 💡
Diversity vs. Convergence
❌ Single Prompt Approach
“Give me the best hypothesis for oat milk fermentation” → One idea, possibly mediocre
✅ Our Multi-Step Approach
- Generate: “Give me 5 hypotheses” → Diversity
- Evaluate: “Score each for feasibility” → Analysis
- Select: “Recommend the top 3” → Quality
Result: Multiple perspectives + Critical evaluation = Stronger research
Good AI Outputs Look Like This:
✅ Structure & Detail - Organized sections - Specific numbers/examples - Academic language - Proper formatting
✅ Actionable Content - Clear next steps - Testable hypotheses - Realistic timelines - Measurable outcomes
Bad AI Outputs Look Like This:
❌ Vague & Generic - “Consider various factors…” - “This is an important topic…” - “Results may vary…”
❌ Incomplete - Missing key sections - No specific examples - Unclear methodology
The 10-Step Research Workflow 🔬
From Idea to Manuscript
Discovery Phase (Steps 1-5) ⏱️ ~60 minutes 1. Idea Generation - Brainstorm hypotheses (8 min) 2. Parallel Exploration - Diversify ideas (12 min) 3. Preliminary Testing - Feasibility checks (10 min) 4. Optimization - Find best parameters (15 min) 5. Full Execution - Main study design (15 min)
The 10-Step Research Workflow (cont’d) 📝
From Idea to Manuscript
Communication Phase (Steps 6-10) ⏱️ ~40 minutes
- Component Analysis - What matters most? (8 min)
- Visualization - Create figures & charts (10 min)
- Writing - Draft manuscript (12 min)
- Review - Peer review simulation (5 min)
- Iteration - Continuous improvement (5 min)
Total time: ~100 minutes for a complete research project from idea to first draft!
This mirrors the actual scientific method - we’re just using AI as our assistant!
Step 1: Idea Generation 🌱
The Power of Structured Brainstorming
Prompt Template:
You are an AI research scientist specializing in Food Science.
Given the following research area, generate 5 distinct and
innovative scientific hypotheses suitable for a Masters-level
research paper.
For each hypothesis, include:
- A clear Title
- 3-5 Keywords
- A short Abstract (under 200 words)
- An explanation of its Novelty and Significance
Research Area: [YOUR TOPIC HERE]
Pro tip: Replace “Food Science” with your specific field for better results!
Notice how specific this prompt is. We’re not just asking for “ideas” - we’re asking for structured, academic hypotheses.
Step 2-3: Exploration & Feasibility 🔍
Step 2: Parallel Exploration (12 min)
- Open multiple chat sessions
- Generate non-overlapping ideas
- Score and rank all options
- Output: 10-15 diverse hypotheses
Step 3: Preliminary Testing (10 min)
- Select top hypothesis
- Design minimal experiment
- Generate expected data
- Output: Feasibility confirmed
Key insight: Step 2 prevents tunnel vision - you see ALL possibilities before committing!
Steps 4-6: The Research Core 🔬
Building Your Study
Step 4: Optimization (15 min) - Test variable combinations - Define success criteria - Find the “sweet spot”
Step 5: Full Execution (15 min) - Detailed methodology - Comprehensive data tables - Statistical measures
Step 6: Component Analysis (8 min) - What ingredients matter? - Ablation studies - Understanding mechanisms
Steps 7-10: Communication & Refinement 📊
Step 7: Visualization (10 min) - Generate scientific figures - Write clear captions - Visual storytelling
Step 8: Manuscript Writing (12 min) - Complete paper draft - All sections included - Proper citations
Step 9: Peer Review (5 min) - Critical evaluation - Scoring rubric - Actionable feedback
Step 10: Iteration (5 min) - Address weaknesses - Refine sections - Achieve excellence
When AI Goes Wrong: Real Examples 🚨
Hallucination Alert!
Made-up Citations: > “According to Smith et al. (2023), fermentation at 45°C increases yield by 23%”
Reality: Paper doesn’t exist!
Plausible but Wrong Data: > “Oat milk contains 15g protein per 100ml”
Reality: Usually 1-3g per 100ml
What You Should Do:
- Always verify numerical claims
- Check citations before using them
- Cross-reference with reliable sources
- Use AI for structure, not facts
Remember: AI is confident even when wrong!
Ethical Guidelines: Using AI in Academic Work 📜
The Right Way to Cite AI Assistance
In your methods section: > “Hypothesis generation and experimental design were developed with assistance from Microsoft Copilot (Microsoft Corporation, 2024). All outputs were verified against peer-reviewed literature.”
What Requires Citation:
- ✅ Idea generation
- ✅ Statistical analysis suggestions
- ✅ Writing structure
- ✅ Data interpretation ideas
What Doesn’t:
- Grammar checking
- Simple calculations
- Basic formatting
Bottom line: When in doubt, cite it. Transparency builds trust.
Live Demo Time! 🚀
Your Choice: Which Research Should We Explore?
Option A: 🥛 Fermentation Study - Oat milk fermentation - pH, cell counts, viscosity - 3 treatments over 48 hours
Option B: 📅 Shelf Life Analysis - Yogurt alternatives - Microbial & sensory data - 4 products over 30 days
Option C: 👅 Sensory Panel - Plant-based cheese - 50 panelists - Texture, flavor, preference
Option D: 🧪 Process Optimization - Protein extraction yields - Temperature vs pH effects - Response surface data
🗳️ Vote Now!
Scan QR code or shout out your choice!
Quick Demo: Bad vs. Good Prompting 🎯
Let’s try both approaches… (10 minutes total)
First: The “Everything” Prompt (3 min)
Watch what happens when we ask for too much at once
“Analyze [winning dataset], create graphs, write conclusions, and suggest future research”
Then: Our Structured Approach (7 min)
See the difference when we break it down
Steps 1 → 3 → 7 in sequence
Pay attention to: Response quality, detail level, and actionability
Troubleshooting Common Issues 🔧
When Things Don’t Work
Problem: AI gives generic responses - Solution: Add more specific requirements to your prompt
Problem: Getting confused by long conversations - Solution: Start a new chat session
Problem: AI refuses to help - Solution: Rephrase more academically, avoid trigger words
Universal fix: When stuck, start fresh with a clearer, more specific prompt
Understanding AI Limitations ⚠️
Critical Things to Know
Hallucinations - AI can “make up” information - Fake citations are common - Always verify sources - May invent plausible-sounding data
Other Limitations - Knowledge cutoff dates - Can’t access real databases - No actual lab work - Context window limits
Golden Rule for Research
Never trust, always verify! Use AI for ideation and structure, but validate all facts, citations, and data.
Key Takeaways 🎓
- One Task, One Prompt - Your golden rule
- Structure = Success - Guide the AI step-by-step
- Iterate & Refine - Fix what needs fixing
- Think Like a Manager - You’re the boss!
- Always Verify - AI assists, you validate
Next Session Preview 👀
- Hands-on practice with all 10 steps
- Building your own AI research agent
- Advanced techniques and shortcuts
- Bring a research topic you’re curious about!
Your Mission (Should You Choose to Accept) 🎯
Before Next Session:
- Think of a research topic you’re interested in
- Try the idea generation prompt on your own
- Note what works and what doesn’t
- Bookmark copilot.microsoft.com or your preferred AI tool
Remember
You’re not learning to use AI - you’re learning to manage AI
Questions? 🤔
Let’s Discuss!
- Concerns about the workflow?
- Technical questions?
- Want to see another demo?
- Ethical considerations?
📧 Contact: michael.borck@curtin.edu.au
Bonus Slide: The Complete Workflow 📋
Your Research Assistant Checklist
| Step | Task | Time | Output |
|---|---|---|---|
| 1 | Idea Generation | 8 min | 5 hypotheses table |
| 2 | Parallel Exploration | 12 min | 10-15 total ideas |
| 3 | Feasibility Testing | 10 min | Experimental plan + data |
| 4 | Optimization | 15 min | Best parameters |
| 5 | Full Study | 15 min | Complete methodology |
| 6 | Component Analysis | 8 min | Key factors identified |
| 7 | Visualization | 10 min | Figures + captions |
| 8 | Writing | 12 min | Full paper draft |
| 9 | Review | 5 min | Feedback report |
| 10 | Iteration | 5 min | Refined manuscript |
This is a reference slide students can photograph or refer back to.
Resources & Links 📚
Everything You Need
- Presentation slides: [GitHub link will be here]
- All datasets: [GitHub data folder link]
- Audio version: [NotebookLM podcast link]
- Video explainer: [NotebookLM video link]
- Quick reference: [Prompt templates document]
Created with assistance from Claude (Anthropic)